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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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- fr |
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- ko |
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- ja |
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- de |
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- it |
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- pt |
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base_model: |
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- black-forest-labs/FLUX.1-schnell |
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pipeline_tag: text-to-image |
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library_name: diffusers |
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--- |
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![FLUX.1 [schnell] Grid](./PEA-Diffusion.png) |
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`MultilingualFLUX.1-adapter` is a multilingual adapter tailored for the Flux.1 series models, theoretically, it inherits ByT5 and can support over 100 languages, but with additional optimizations in Chinese. Originating from an ECCV 2024 paper titled [PEA-Diffusion](https://arxiv.org/abs/2311.17086). The open-source code is available at https://github.com/OPPO-Mente-Lab/PEA-Diffusion. |
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# Usage |
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We used the multilingual encoder [byt5-xxl](https://huggingface.co/google/byt5-xxl/tree/main), and the teacher model used in the adaptation process was FLUX.1-schnell. We implemented a reverse denoising process for distillation training. The adapter can be applied to any FLUX.1 series model in theory. Here we provide the following application examples. |
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## `MultilingualFLUX.1` |
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The same applies to other FLUX.1 series models, just remember to adjust the `num_inference_steps` and `guidance_scale` as needed. |
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```python |
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from diffusers import FluxPipeline, AutoencoderKL |
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from diffusers.image_processor import VaeImageProcessor |
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from transformers import T5ForConditionalGeneration,AutoTokenizer |
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import torch |
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import torch.nn as nn |
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class MLP(nn.Module): |
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def __init__(self, in_dim=4096, out_dim=4096, hidden_dim=4096, out_dim1=768, use_residual=True): |
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super().__init__() |
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self.layernorm = nn.LayerNorm(in_dim) |
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self.projector = nn.Sequential( |
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nn.Linear(in_dim, hidden_dim, bias=False), |
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nn.GELU(), |
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nn.Linear(hidden_dim, hidden_dim, bias=False), |
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nn.GELU(), |
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nn.Linear(hidden_dim, out_dim, bias=False), |
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) |
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self.fc = nn.Linear(out_dim, out_dim1) |
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def forward(self, x): |
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x = self.layernorm(x) |
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x = self.projector(x) |
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x2 = nn.GELU()(x) |
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x1 = self.fc(x2) |
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x1 = torch.mean(x1,1) |
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return x1,x2 |
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dtype = torch.bfloat16 |
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device = "cuda" |
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ckpt_id = "black-forest-labs/FLUX.1-schnell" |
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text_encoder_ckpt_id = 'google/byt5-xxl' |
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proj_t5 = MLP(in_dim=4672, out_dim=4096, hidden_dim=4096, out_dim1=768).to(device=device,dtype=dtype) |
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text_encoder_t5 = T5ForConditionalGeneration.from_pretrained(text_encoder_ckpt_id).get_encoder().to(device=device,dtype=dtype) |
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tokenizer_t5 = AutoTokenizer.from_pretrained(text_encoder_ckpt_id) |
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proj_t5_save_path = f"diffusion_pytorch_model.bin" |
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state_dict = torch.load(proj_t5_save_path, map_location="cpu") |
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state_dict_new = {} |
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for k,v in state_dict.items(): |
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k_new = k.replace("module.","") |
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state_dict_new[k_new] = v |
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proj_t5.load_state_dict(state_dict_new) |
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pipeline = FluxPipeline.from_pretrained( |
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ckpt_id, text_encoder=None, text_encoder_2=None, |
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tokenizer=None, tokenizer_2=None, vae=None, |
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torch_dtype=torch.bfloat16 |
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).to(device) |
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vae = AutoencoderKL.from_pretrained( |
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ckpt_id, |
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subfolder="vae", |
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torch_dtype=torch.bfloat16 |
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).to(device) |
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vae_scale_factor = 2 ** (len(vae.config.block_out_channels)) |
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image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor) |
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while True: |
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raw_text = input("\nPlease Input Query (stop to exit) >>> ") |
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if not raw_text: |
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print('Query should not be empty!') |
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continue |
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if raw_text == "stop": |
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break |
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with torch.no_grad(): |
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text_inputs = tokenizer_t5( |
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raw_text, |
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padding="max_length", |
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max_length=256, |
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truncation=True, |
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add_special_tokens=True, |
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return_tensors="pt", |
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).input_ids.to(device) |
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text_embeddings = text_encoder_t5(text_inputs)[0] |
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pooled_prompt_embeds,prompt_embeds = proj_t5(text_embeddings) |
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height, width = 1024, 1024 |
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latents = pipeline( |
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prompt_embeds=prompt_embeds, |
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pooled_prompt_embeds=pooled_prompt_embeds, |
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num_inference_steps=4, guidance_scale=0, |
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height=height, width=width, |
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output_type="latent", |
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).images |
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latents = FluxPipeline._unpack_latents(latents, height, width, vae_scale_factor) |
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latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor |
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image = vae.decode(latents, return_dict=False)[0] |
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image = image_processor.postprocess(image, output_type="pil") |
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image[0].save("MultilingualFLUX.jpg") |
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``` |
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## `MultilingualOpenFLUX.1` |
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[OpenFLUX.1] (https://huggingface.co/ostris/OpenFLUX.1) is a fine tune of the FLUX.1-schnell model that has had the distillation trained out of it. |
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Please be sure to update the path of fast-lora.safetensors you have downloaded in the following code. |
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```python |
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from diffusers import FluxPipeline, AutoencoderKL |
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from diffusers.image_processor import VaeImageProcessor |
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from transformers import T5ForConditionalGeneration,AutoTokenizer |
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import torch |
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import torch.nn as nn |
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class MLP(nn.Module): |
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def __init__(self, in_dim=4096, out_dim=4096, hidden_dim=4096, out_dim1=768, use_residual=True): |
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super().__init__() |
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self.layernorm = nn.LayerNorm(in_dim) |
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self.projector = nn.Sequential( |
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nn.Linear(in_dim, hidden_dim, bias=False), |
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nn.GELU(), |
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nn.Linear(hidden_dim, hidden_dim, bias=False), |
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nn.GELU(), |
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nn.Linear(hidden_dim, out_dim, bias=False), |
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) |
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self.fc = nn.Linear(out_dim, out_dim1) |
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def forward(self, x): |
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x = self.layernorm(x) |
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x = self.projector(x) |
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x2 = nn.GELU()(x) |
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x1 = self.fc(x2) |
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x1 = torch.mean(x1,1) |
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return x1,x2 |
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dtype = torch.bfloat16 |
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device = "cuda" |
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ckpt_id = "ostris/OpenFLUX.1" |
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text_encoder_ckpt_id = 'google/byt5-xxl' |
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proj_t5 = MLP(in_dim=4672, out_dim=4096, hidden_dim=4096, out_dim1=768).to(device=device,dtype=dtype) |
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text_encoder_t5 = T5ForConditionalGeneration.from_pretrained(text_encoder_ckpt_id).get_encoder().to(device=device,dtype=dtype) |
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tokenizer_t5 = AutoTokenizer.from_pretrained(text_encoder_ckpt_id) |
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proj_t5_save_path = f"diffusion_pytorch_model.bin" |
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state_dict = torch.load(proj_t5_save_path, map_location="cpu") |
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state_dict_new = {} |
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for k,v in state_dict.items(): |
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k_new = k.replace("module.","") |
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state_dict_new[k_new] = v |
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proj_t5.load_state_dict(state_dict_new) |
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pipeline = FluxPipeline.from_pretrained( |
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ckpt_id, text_encoder=None, text_encoder_2=None, |
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tokenizer=None, tokenizer_2=None, vae=None, |
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torch_dtype=torch.bfloat16 |
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).to(device) |
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pipeline.load_lora_weights("ostris/OpenFLUX.1/openflux1-v0.1.0-fast-lora.safetensors") |
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vae = AutoencoderKL.from_pretrained( |
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ckpt_id, |
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subfolder="vae", |
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torch_dtype=torch.bfloat16 |
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).to(device) |
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vae_scale_factor = 2 ** (len(vae.config.block_out_channels)) |
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image_processor = VaeImageProcessor(vae_scale_factor=vae_scale_factor) |
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while True: |
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raw_text = input("\nPlease Input Query (stop to exit) >>> ") |
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if not raw_text: |
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print('Query should not be empty!') |
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continue |
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if raw_text == "stop": |
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break |
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with torch.no_grad(): |
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text_inputs = tokenizer_t5( |
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raw_text, |
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padding="max_length", |
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max_length=256, |
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truncation=True, |
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add_special_tokens=True, |
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return_tensors="pt", |
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).input_ids.to(device) |
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text_embeddings = text_encoder_t5(text_inputs)[0] |
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pooled_prompt_embeds,prompt_embeds = proj_t5(text_embeddings) |
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height, width = 1024, 1024 |
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latents = pipeline( |
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prompt_embeds=prompt_embeds, |
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pooled_prompt_embeds=pooled_prompt_embeds, |
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num_inference_steps=4, guidance_scale=0, |
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height=height, width=width, |
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output_type="latent", |
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).images |
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latents = FluxPipeline._unpack_latents(latents, height, width, vae_scale_factor) |
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latents = (latents / vae.config.scaling_factor) + vae.config.shift_factor |
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image = vae.decode(latents, return_dict=False)[0] |
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image = image_processor.postprocess(image, output_type="pil") |
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image[0].save("MultilingualOpenFLUX.jpg") |
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``` |
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To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation |
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# License |
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The adapter itself is Apache License 2.0, but it must follow the license of the main model, such as FLUX.1 [dev] Non Commercial License. |
